Professional Certificate in Model Performance Metrics Essentials
-- viewing nowThe Professional Certificate in Model Performance Metrics Essentials is a comprehensive course that equips learners with the indispensable skills required to evaluate and improve machine learning models' performance. This program is crucial in today's data-driven world, where businesses rely on accurate and efficient models to make informed decisions.
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Course Details
• Model Accuracy Metrics
• Regression Metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared
• Classification Metrics: Confusion Matrix, Accuracy, Precision, Recall, F1 Score, ROC Curve, AUC
• Cross-validation Techniques: K-Fold Cross-Validation, Stratified K-Fold Cross-Validation
• Handling Imbalanced Datasets: Oversampling, Undersampling, SMOTE
• Model Selection Metrics: Fit Score, Score Method, GridSearchCV, RandomizedSearchCV
• Performance Monitoring: Training and Testing Splits, Time Series Analysis, Model Drift
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Entry Requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course Status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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